Existing depression screening predominantly relies on standardized
questionnaires (e.g., PHQ-9, BDI), which suffer from high misdiagnosis rates
(18-34% in clinical studies) due to their static, symptom-counting nature and
susceptibility to patient recall bias. This paper presents an AI-powered
depression prevention system that leverages large language models (LLMs) to
analyze real-time conversational cues–including subtle emotional expressions
(e.g., micro-sentiment shifts, self-referential language patterns)–for more
accurate and dynamic mental state assessment. Our system achieves three key
innovations: (1) Continuous monitoring through natural dialogue, detecting
depression-indicative linguistic features (anhedonia markers, hopelessness
semantics) with 89% precision (vs. 72% for PHQ-9); (2) Adaptive risk
stratification that updates severity levels based on conversational context,
reducing false positives by 41% compared to scale-based thresholds; and (3)
Personalized intervention strategies tailored to users’ emotional granularity,
demonstrating 2.3x higher adherence rates than generic advice. Clinical
validation with 450 participants shows the system identifies 92% of at-risk
cases missed by traditional scales, while its explainable AI interface bridges
the gap between automated analysis and clinician judgment. This work
establishes conversational AI as a paradigm shift from episodic scale-dependent
diagnosis to continuous, emotionally intelligent mental health monitoring.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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